Memories for third-person experiences in immersive virtual reality
Bibliographic record
Abstract
We typically experience the world from a first-person perspective (1PP) but can sometimes experience events from a third-person perspective (3PP) much as an observer might see us. Little is known about how visual perspective influences the formation of memories for events. We developed an immersive virtual reality paradigm to examine how visual perspective during encoding influences memories. Across two studies, participants explored immersive virtual environments from first-person and third-person avatar perspectives while wearing an Oculus Rift headset. Memory was tested immediately (Study One and Study Two) and following a one-week delay (Study Two). We assessed the accuracy of visual memory using cued recall questions and spatial memory by asking participants to draw maps of the layout of each environment (Study One and Study Two). Additional phenomenological ratings were included to assess visual perspective during remembering (Study Two). There were no differences in the accuracy of visual information across the two studies, but 3PP experiences were found to increase spatial memory accuracy due to their wider camera field of view when compared to 1PP experiences. Our results also demonstrate that 3PP experiences create 3PP memories, as reflected by an increase in subjective ratings of observer-like perspectives during remembering. In sum, visual perspective during memory formation influences the accuracy of spatial but not visual information, and the vantage point of memories during remembering.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".